Objectives: The objective of this study was to assess the current usage, challenges, and specific needs regarding biomechanical data, with a focus on gait analysis, across a diverse field such as sports performance, clinical rehabilitation, and occupational health. Methods: We conducted a survey among more than 300 institutions in the DACH region (Germany, Austria, Switzerland). Of 49 individuals who began the survey, 24 submitted complete responses. Results: Findings indicate that participants recognize the value of biomechanical data, yet adoption of publicly available datasets is hindered by inconsistent formats, limited metadata, and strict privacy regulations. Respondents emphasized standardized documentation and clearer guidelines to facilitate sharing and collaboration. Many institutions are overwhelmed by legal compliance, with data proception laws posing significant hurdles to effective usage. Conclusions: The results highlight the need to (1) establish standardized file formats, (2) improve metadata quality, (3) develop transparent, consistent data-sharing protocols, and (4) clarify legal frameworks for compliance.

Understanding the decision-making behavior of machine learning models is essential in domains where individual predictions matter, such as medical diagnosis or sports analytics. While explainable artificial intelligence (XAI) methods such as SHAP provide instance-level feature attributions, they mainly summarize typical decision behavior and offer limited support for systematically exploring atypical yet correctly classified cases. In this work, we introduce the Classification Outlier Variability Score (COVAS), a framework designed to support hypothesis generation through the analysis of explanation variability. COVAS operates in the explanation space and builds directly on SHAP value representations. It quantifies how strongly an individual instance’s SHAP-based explanation deviates from class-specific attribution patterns by aggregating standardized SHAP deviations into a single score. Consequently, the applicability of COVAS inherits the model- and data-agnostic properties of SHAP, provided that explanations can be computed for the underlying model and data. We evaluate COVAS on publicly available datasets from the medical and sports domains. The results show that COVAS reveals explanation-space outliers not captured by feature-space outlier detection or prediction uncertainty measures. Robustness analyses demonstrate stability across parameter choices, class imbalance, model initialization, and model classes. Overall, COVAS complements existing XAI techniques by enabling targeted instance-level inspection and facilitating XAI-guided hypothesis formulation.